Gradually Moving to a new domain:
Artificial Intelligence, Machine Learning (especially unsupervised learning, semi-supervised learning, meta learning, data mining, heuristics).
Applications: e-Learning (especially collaborative learning, informal learning), Information Retrieval (especially information extraction, natural language processing NLP), Personalization (collaborative filtering, recommender systems, user modeling, peer modeling).
N. Rubens, D. Kaplan, M. Sugiyama. Recommender Systems Handbook: Active Learning (eds. P.B. Kantor, F. Ricci, L. Rokach, B. Shapira). Springer. [to be published Jul.2010]. web
M. Sugiyama, N. Rubens, and K.-R. Müller. Dataset Shift in Machine Learning: A conditional expectation approach to model selection and active learning under covariate shift. MIT Press, Cambridge, 2009. pdf web
N. Rubens, K. Still, J. Huhtamaki and M. G. Russell, "Investment Firms as Resource Routers in an Innovation Ecology", Journal of Networks, [to be published].
Rubens, N., Kaplan, D., & Okamoto, T. (2011). ELIxIR: Expertise Learning and Identification x Information Retrieval. International Journal of Information Systems and Social Change (IJISSC), 2(1), 48-63. doi:10.4018/jissc.2011010104
N. Rubens, D. Kaplan, M. Villenius, and T. Okamoto, “CAFE: Collaboration Aimed at Finding Experts”, International Journal of Knowledge and Web Intelligence 2010 - Vol. 1, No.3/4 pp. 169 - 186. doi:10.1504/IJKWI.2010.034186
Huhtamaki, J., Russell, M., Still, K., & Rubens, N., "A Network-Centric Snapshot of Value Co-Creation in Finnish Innovation Financing", Open Source Business Resource Journal, Mar 2011.
N. Rubens, R. Tomioka, and M. Sugiyama, “Output divergence criterion for active learning in collaborative settings,” IPSJ Transactions on Mathematical Modeling and Its Applications, vol. 2, pp. 87 — 96, 12 2009.
M. Vilenius, N. Rubens, F. Anma, and T. Okamoto, "Supporting Collaborative Activities in Informal, Ill-constructed Learning", The Journal of Information and Systems in Education, Vol. 8, Nr. 1, pp. 12-24, 2009.
M. Sugiyama and N. Rubens. A batch ensemble approach to active learning with model selection. Neural Networks, 2008. pdf
Papers / Presentations
M. G. Russell, J. Flora, M. Strohmaier, J. Poschko, R. Perez, N. Rubens. Semantic Analysis of Energy-Related Conversations in Social Media: A Twitter Case Study. International Conference of Persuasive Technology (Persuasive 2011), Columbus, OH, USA, Jun.2011.
N. Rubens, M. Russell, R. Perez, J. Huhtamäki, K. Still, D. Kaplan, and T. Okamoto. Alumni network analysis. In IEEE Education Engineering (EDUCON 2010), Apr 2011.
M. Russell, C. Yu, K. Still, J. Huhtamaki, R. Perez, and N. Rubens, "Value Co-Creation Networks and Social Media Conversations in the Gree Tech Innovation Ecosystem", Behavior, Energy and Climate Change Conference (BECC), Washington DC, Nov.29 - Dec.2, 2010 (slides).
N. Rubens, "Making Innovation Work", Transactions of JSISE, vol. 28, no. 1, 2011.
N. Rubens, D. Kaplan, T. Okamoto, "WE: Web of Experts", Computers and Advanced Technology in Education (CATE 2010), 2010.
N. Rubens, V. Sheinman, T. Okamoto, M. Ueno, "Active Learning for Black-box Models", International Conference Computer Data Analysis and Modeling: Complex Stochastic Data and Systems (CDAM 2010), 2010.
R. P. Mendoza, N. Rubens, T. Okamoto, "Hierarchical Aggregation Prediction Method", KDD Cup 2010 Workshop, ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 2010), Jul.2010.
J. Huhtamäki, M. G. Russell, N. Rubens and K. Still, "Data-Driven Analysis of Co-Creation in Innovation Ecosystems", EBRF, Nokia, Finland, September 15-16, 2010.
N. Rubens, T. Okamoto, and M. G. Russell, “Journalistic research tools: How to spot trends in innovation ecosystems by visualizing data,” in The Seventh Conference on Innovation Journalism (IJ-7), Stanford University, Jun.2010. [invited presentation]
N. Rubens, K. Still, J. Huhtamaki and M. G. Russell, "Investment Firms as Resource Routers in an Innovation Ecology", International Symposium on Electronic Commerce and Security (ISECS 2010), IEEE.
N. Rubens, T. Okamoto, and M. G. Russell, “New Tools for Analysis: Innovation Ecosystems DataSet,” in Media X: Social Network Analysis: New Tools and Data, Stanford University, Jun.2010.
N. Rubens and T. Okamoto, “Getting answers to questions we haven’t yet asked,” in Media X: Innovation Ecosystem Networks, Stanford University, Mar. 2010.
N. Rubens, K. Still, J. Huhtamaki, and M. G. Russell, “Leveraging social media for analysis of innovation players and their moves,” tech. rep., Media X, Stanford University, Feb. 2010.
N. Rubens, M. Vilenius, T. Okamoto. Automatic Group Formation for Informal Collaborative Learning. SPeL 2009. In Proceedings of IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology, 2009. WI-IAT '09.
N. Rubens, M. Vilenius, T. Okamoto. Data-driven Group Formation for Informal Collaborative Learning. E-Learn 2009. AACE. 2009
N. Rubens, M. Vilenius, and T. Okamoto, “Automatic group formation for informal collaborative learning,” in 34th Conference of Japanese Society for Information and Systems in Education (JSiSE), 2009.
N. Rubens, M. Vilenius, and T. Okamoto, “Mashup-based group formation for informal learning,” in 25th Conference of the Japan Society for Educational Technology (JSET), 2009.
M. V. Jankovic and N. Rubens, “A new probabilistic approach to on-line learning in artificial neural networks,” in ASMCSS’09: Proceedings of the 3rd International Conference on Applied Mathematics, Simulation, Modeling, Circuits, Systems and Signals, 2009.
N. Rubens, T. Okamoto. Social Mobile Services: Japanese Perspective. MediaX 2009: New Metrics for New Media. Stanford University. 2009.
M. Russell, N. Rubens, J. Huhtamäki, C. Duan. Strategies & Practices in International Alliances for National and Regional Information Communication Technologies Industries. MediaX 2009: Visualization for Collective, Connective and Distributed Intelligence. Stanford University. 2009.
N. Rubens, T. Okamoto, and M. Ueno, “Model-agnostic active learning,” in Proceedings of the Japanese Society for Artificial Intelligence 69th Meeting of Special Interest Group on Fundamental Problem in Artificial Intelligence, 2009.
N. Rubens and M. Ueno, “Estimate response-based active learning,” in Annual Workshop of the Behaviormetric Society of Japan (BSJ 2009), 2009.
M. Vilenius, N. Rubens and F. Anma, "Recommending collaborative activities in informal learning using Bayesian methodology", Technical Committee on Advanced Learning Science and Technology (SIG-ALST-A901-03), pp. 15-20, 07.2009.
N. Rubens, V. Sheinman, T. Tokunaga, and M. Sugiyama. Order retrieval. In Proccedings of the 3rd International Conference on Large-scale Knowledge Resources (LKR 2008), Lecture Notes in Artificial Intelligence (LNAI). Springer-Verlag, 2008. pdf web
M. Sugiyama and N. Rubens. Active learning with model selection in linear regression. In SIAM International Conference on Data Mining (SDM 2008), 2008. pdf
N. Rubens and M. Sugiyama. Influence-based collaborative active learning. In Proceedings of the 2007 ACM conference on Recommender systems (RecSys 2007). ACM, 2007. pdf
N. Rubens and M. Sugiyama. Explorative active learning for collaborative filtering. In Proceedings of the Japanese Society for Artificial Intelligence 67th Meeting of Special Interest Group on Fundamental Problem in Artificial Intelligence, 2007. pdf
V. Sheinman, N. Rubens, and T. Tokunaga. Commonly perceived order within a category. In Proceeding of OntoLex Workshop at 6th International Semantic Web Conference (ISWC 07 ), 2007. pdf
V. Sheinman, N. Rubens, and T. Tokunaga. Word sequences for second language acquisition. MAPLL Workshop. Technical report of IEICE. Thought and language, 107(138), 2007.
N. Rubens and M. Sugiyama. Coping with active learning with model selection dilemma: Minimizing expected generalization error. In Proceedings of 2006 Workshop on Information-Based Induction Sciences (IBIS 2006), 2006. pdf bib
Domestic (Japanese) Conferences
N. Rubens, D. Kaplan, T. Okamoto, "Lending a Helping Hand: Framework for Web-based Expertise Finding", Conference of Educational System Information Society (JSiSE), 2010.
M. G. Russell, N. Rubens, C. Yu, "Internal and External Innovation Ecosystems in China 2.0", Stanford Program on Regions of Innovation and Entrepreneurship (SPRIE), Stanford University, May 10, 2011.
N. Rubens. Active Mining. Research Meeting. Nagaoka University of Technology, 2009 [invited speaker].
N. Rubens. DNA sequence alignment with the use of reinforcement learning. Technical report, 2004.
N. Rubens. Application of fuzzy logic to the refinement of an answer set of information retrieval systems. Technical report, 2004.
N. Rubens. Detecting network intrusion with the use of belief networks. Technical report, 2003.
Best Technology Paper Award ISPIM 2012 (sponsored by Nokia Siemens), (co-author).
Best Paper Award ICMB 2012 (co-author).
Best Presentation Award ICMB 2012 (co-author).
Best Paper Award CATE 2012 (co-author, received by my PhD student Sebastien).
Best Paper Award JSiSE 2009.
Research Fellowship Apr.2007 – Oct.2008
Japanese Government (MEXT (Monbukagasho) / Tokyo Institute of Technology) $50,000
Research Fellowship Oct.2005 – Apr.2007
Japanese Government (MEXT (Monbukagasho) / Tokyo Institute of Technology) $50,000
Research Fellowship Oct.2005 – Oct.2008
Japanese Government (MEXT (Monbukagasho) / JAIST) $100,000
Declined due to conflicting offer
Travel / Conference Grant Oct.2006
Information-Based Induction Sciences (IBIS)
Wan Xin (PhD 2011) *1
Mikko Vilenius (PhD) *1
Rafael Perez (MS 2009 & PhD) *1
*1: co-supervised with Prof. Okamoto
Reviewer: MIT Press Books (paid reviewer), Neurocomputing, Behaviormetrika
Editorial Board Member (Springer)
ISECS 2010 International Symposium on Electronic Commerce and Security (IEEE)
Program Committee Member:
Inconsistency Robustness 2011 Symposium, Stanford University, CA, USA
International Workshop on Ubiquitous Human-Computer Interaction - Ubi HCI 2011
Bionetics 2010, Special Track on Artificial Life and Bio-inspired Robotics (ACM SIGSIM)
International Workshop on Advanced Methodologies for Bayesian Networks (AMBN 2010, JSAI)
Tokyo Institute of Technology
PhD Computer Science
Thesis: Collaborative Active Learning
Machine Learning Lab
Supervisor: Prof. Sugiyama
University of Massachusetts
MSc Computer Science
Thesis: Fuzzy Ranking for Information Retrieval Systems
Supervisors: Prof. Alan, Prof. Kilmer
Brigham Young University
BSc Computer Science
University of Electro Communications, Graduate School of Information Science
Feb.2009 – present
Affiliations: Center for Frontier Science and Engineering, AI Lab (Okamoto/Ueno), Center for Developing E-Learning
Tokyo Institute of Technology, Machine Learning Lab (Sugiyama) Oct.2005 – Feb.2009
Artificial Intelligence/Machine Learning: development of fundamental theories and practical algorithms
Fincross May.2003 – Aug.2005
Senior Software Engineer (Consultant)
Application of Artificial Intelligence and Information Retrieval to Financial Analysis
CTS / Bell Dec.2001–May.2003
Analysis and processing of telecommunication data
Development of server side applications with high reliability/scalability requirements
Database Engine optimization, Analysis and processing of standardized assessment data
Brigham Young University / Nissan Sep.1998–May.1999
R&D of neuro-fuzzy controller for Accumulative Cruise Control
Primarily responsible for maintaining and improving Word Perfect Expert module – combines the features from QuickTasks, templates, Help, and coaches user to help accomplish work more efficiently
Frank Phillips College Aug.1996–May.1997
Developing and maintaining IT infrastructure
Training/Supervising teaching assistants
Software Engineer primarily responsible for (NIC):
▪ DMV Renewal (revenue: $10 Million / year)
▪ Payment Processing Framework (revenue: $20 Million / year)
▪ Shopping Cart (used by several US state agencies)
Data Analysis / Processing
▪ financial data (Fincross)
▪ standardized assessment data (ETS)
▪ telecommunication data (CTS)
▪ Numerical Analysis: Matlab, R, SAS, SPSS, RapidMiner, Weka, Tableau
▪ Programming Languages: Java EE (Enterprise Edition), Python, C#, C++
▪ Database: design and implementation, Oracle, MySQL, SQL
▪ Information Retrieval: Apache Lucene, Solr, Lemur
▪ Frameworks: Grid/Cloud Computing (Hadoop (Pig, Hive, Mahout), Google App Engine [GAE], Amazon Elastic Cloud [EC]), Design Patterns, UML, Unit Testing, Extreme Programming